ArticleJournal of racial and ethnic health disparities2026
Measuring the Impact of Data Quality and Computable Phenotypes on Potential Racial Disparities in Predicting Healthcare Utilization Among Type 2 Diabetes Populations.
Article in Journal of racial and ethnic health disparities, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Article
- Comparing computable type 2 diabetes phenotype definitions in identifying populations of interest for clinical research.BMJ health & care informatics · 2025Article
- Assessing the Impact of Computable Type 2 Diabetes Phenotypes on Predicting Healthcare Utilization Using Electronic Health Records and Administrative Claims.Healthcare (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
introductionType 2 diabetes (T2D) computable phenotypes identify different denominator populations for downstream tasks. Differences in racial composition could introduce bias and lead to disparate disease management. The objective of this study was to assess potential racial disparities in predicting T2D healthcare utilization introduced by data quality and computable phenotypes.
methodsFour published and one local T2D phenotypes were applied to the EHR and claims datasets of a large academic medical center. Population characteristics were compared across phenotypes, stratified by race. We induced data incompleteness, inaccuracy, and untimeliness to measure the impact on denominator racial composition. We trained logistic classification models on each of the phenotype-specific populations separately and compared disparities in utilization prediction (i.e., inpatients (IP) and emergency room (ER) admissions). Model performance, such as mean AUC and positive/negative predictive values, were compared across phenotypes, stratified by race.
resultsDifferent T2D computable phenotypes identified populations with modestly different racial compositions. Black T2D patients had the highest average admissions to ER compared to other racial groups. Induced data quality challenges diminished patient counts across all racial groups proportionally. Charlson comorbidity score had the highest odds ratio in predicting IP and ER admissions across phenotypes and race groups. Specific T2D phenotypes showed the highest and lowest mean AUCs in predicting IP and ER admissions in Black and White populations; however, such results were not observed among Asian/Other populations.
conclusionUtilization prediction differed among phenotypes and race groups. Understanding the complexities behind phenotypes, data quality, and predictive models could mitigate health disparity further downstream and inform clinical research and disease management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.